Exploratory Data Analysis With Complex Datasets In Python
Released 6/2025
By Anand Saravanan
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 36m | Size: 77 MB
This course will teach you how to perform exploratory data analysis (EDA) on messy, high-dimensional data using Python while navigating real-world challenges.
When datasets have a high number of dimensions and messy data, it can make EDA feel overwhelming and challenging. In this course, Exploratory Data Analysis with Complex Datasets in Python, you'll gain the ability to clean and conduct EDA on messy, high-dimensional data using Python. First, you'll explore what complex datasets are and learn how to use Python to gain more knowledge about the structure of the data and how to visualize this. Next, you'll discover cleaning techniques to prepare complex data such as handling missing values, encoding categorical variables, dimension reduction, etc. Finally, you'll learn how to perform and refine EDA on complex data to find trends and patterns as well as detect pitfalls in the data. When you're finished with this course, you'll have the skills and knowledge of EDA needed to tackle complex datasets and extract insights from it using Python.
Exploratory Data Analysis With Complex Datasets In Python
